Based on the predictions of multiple models, it is straightforward to obtain the segmentation uncertainty that is typically estimated by measuring the diversity of multiple predictions for a given image [1]}. Supposing \(X\)  denotes a test image and \(Y\)  the predicted label of \(X\) . Using the variance and entropy of the distribution \(p(Y|X)\)  are two common methods for uncertainty estimation. Similarly to [2]}, it is a promising choice to estimate uncertainty by ensemble method, using multiple independently learned models. We use the entropy to represent the voxel-wise uncertainty:
\(H(Y|X)=-\int p(y|X)\text{ln}(p(y|X))dy\) 
